Validation of Blood Transfusion Risk Scores (TRACK and TRUST) in a Cardiac Surgery Service in Brazil

Abstract

Introduction Transfusion of red blood cells is recurrent in cardiac surgery despite the well-established deleterious effects. Identifying patients with higher chances of requiring blood transfusion is essential to apply strategic preventive measures to reduce such chances, considering the restricted availability of this product. The most used risk scores to predict blood transfusion are the Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST). However, these scores were not validated for the Brazilian population. The objective of this study was to assess the accuracy of TRACK and TRUST scores in estimating the need for postoperative transfusion of red blood cell concentrates (TRBCC) after cardiac surgery. Methods A clinical retrospective study was conducted using the database of a Brazilian reference service composed of patients operated between November 2019 and September 2021. Scores were compared using Mann-Whitney U test. Hosmer-Lemeshow goodness of fit test assessed calibration of the scores. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC). All analyses considered a level of significance of 5%. The study was approved by the research ethics committee (CAAE 55577421.4.0000.5201). Results This study assessed 498 patients. Only the TRACK score presented good calibration (P=0.238; TRUST P=0.034). AUC of TRACK was 0.678 (95% confidence interval 0.63 to 0.73; P<0.001), showing a significant accuracy. Conclusion Between the scores analyzed, only the TRACK score showed a good calibration, but low accuracy, to predict postoperative TRBCC after cardiac surgery.

Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population

“…Notably, a systematic review revealed the publication of 169 prediction tools utilizing artificial intelligence during the COVID-19 pandemic, highlighting the growing interest in this area [ 16 ] . However, despite the existence of globally utilized risk prediction scores for blood transfusion in cardiac surgery, their validation in the Brazilian population remains insufficient [ 7 ] . Several factors have been proposed to explain this discrepancy, ranging from the unique characteristics of the Brazilian population as a developing country, where anemia prevails at higher rates compared to developed nations, to the limited access of Brazilian patients to globally employed equipment for intraoperative blood reuse.…”

“…Hemoglobin levels have been established as a significant prognostic factor for transfusion requirements, carrying substantial scientific evidence. Numerous studies have consistently revealed a direct correlation between lower preoperative hemoglobin levels and an elevated probability of necessitating transfusions, while conversely, higher hemoglobin levels are associated with a decreased risk [ 3 , 4 , 7 ] . These findings, supported by multiple investigations, emphasize the criticality of diligent monitoring and effective management of hemoglobin levels both before and during surgical interventions as a fundamental approach to diminish transfusion needs [ 19 ] .…”

“…To evaluate the efficacy of existing blood transfusion predictive models, validation studies have been conducted across diverse patient populations addressing their inherent limitations [ 5 , 6 ] . One such study examined the widely used Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) scoring systems, revealing their less-than-optimal accuracy when applied to specific patient cohorts [ 7 ] . This finding highlights the inadequacy and unreliability of these models for all patients and emphasize the need for further research and for the development of more precise and effective models to predict blood transfusion needs.…”

“…The accuracy limitations of the currently available scoring systems can be attributed to variations in patients’ demographics, clinical characteristics, and surgical practices across different populations [ 7 ] . Machine learning (ML) algorithms have the potential to offer more accurate predictions by analyzing complex interactions between patients’ characteristics and surgical factors [ 8 ] , making them a promising approach for improving the accuracy of blood transfusion prediction models.…”

Development of a Novel Prediction Model for Red Blood Cell Transfusion Risk in Cardiac Surgery

“…However, when these models were externally validated in different cohorts, especially in regions such as Latin America and Asia [ 12 , 13 ], discrepant results were observed. Consequently, the ACTA-PORT [ 8 ] scoring system was developed.…”